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Automated personal identification system based on human iris analysis

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Abstract

In general, a typical iris recognition system includes iris imaging, iris liveness detection, iris image quality assessment, and iris recognition. This paper presents an algorithm focusing on the last two steps. The novelty of this algorithm includes improving the speed and accuracy of the iris segmentation process, assessing the iris image quality such that only the clear images are accepted so as to reduce the recognition error, and producing a feature vector with discriminating texture features and a proper dimensionality so as to improve the recognition accuracy and computational efficiency. The Hough transform, polynomial fitting technique, and some morphological operations are used for the segmentation process. The phase data from 1D Log-Gabor filter is extracted and encoded efficiently to produce a proper feature vector. Experimental tests were performed using CASIA iris database (756 samples). These tests prove that the proposed algorithm has an encouraging performance.

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References

  1. Jain KA, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol Spec Issue Image Video Biometrics 14(1):4–20

    Article  Google Scholar 

  2. Flom L (1987) Iris recognition system. Patent no. 4, 641,349

  3. Daugman JG (1994) Biometric personal identification system based on iris analysis. Patent no. 5 291 560

  4. Daugman JG (2002) How iris recognition works. In: Proceedings of 2002 international conference on image processing, vol 1

  5. Wildes RP, Asmuth JC, Hanna JK, Hsu SC, Kolezynski RJ, Matey JR, McBride SE (1996) Automated, non-invasive iris recognition system and method. Patent no. 5,572,596

  6. Wildes RP (1997). Iris recognition: an emerging biometric technology. Proc IEEE 85:1348–1363

    Article  Google Scholar 

  7. Matsushita M (1999) Iris identification system and iris identification method. Patent no. 5,901,238

  8. Lim S, Lee K, Byeon O, Kim T (2001) Efficient iris recognition through improvement of feature vector and classifier. ETRI J 23(2):61–70

    Google Scholar 

  9. Ali JM, Hassanien AE (2003) An iris recognition system to enhance e-security environment based on wavelet theory. Adv Model Optim 5(2):93–104

    MATH  MathSciNet  Google Scholar 

  10. Kamada M (2004) Iris authentication apparatus. Patent no.6,785,406 B1

  11. Ma L, Wang Y, Tan T, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell 25(12):1519–1533

    Article  Google Scholar 

  12. Zhang G, Salganiciff M (1999) Method of measuring the focus of close-up images of eyes. US Patent no.5953440

  13. Boles WW, Boashash B (1998) A human identification technique using images of the iris and wavelet transform. IEEE Trans Signal Process 46(4):1185–1188

    Article  Google Scholar 

  14. Zhu Y, Tan T, Wang Y (2000) Biometric personal identification based on iris patterns. Proc Int Conf Pattern Recognit 2:805–808

    Google Scholar 

  15. Tisse C, Martin L, Torres L, Robert M (2002) Person identification technique using human iris recognition. Proc Vis Interface 294–299

  16. Ma L, Wang Y, Tan T (2002) Iris recognition based on multichannel Gabor filtering. In: Proceedings of ACCV’2002: the 5th Asian conference on computer vision, 23–25 January, Melbourne

  17. Ma L, Wang Y, Tan T (2002) Iris recognition using circular symmetric filters. Proc Int Conf Pattern Recognit 2:414–417

    Google Scholar 

  18. Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  19. CASIA (2003) CASIA iris image database. http://www.sinobiometerics.com

  20. Masek L, Kovesi P (2003) MATLAB source code for a biometric identification system based on iris patterns. The School of Computer Science and Software Engineering, The University of Western Australia

  21. Field D (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am 4:2379–2394

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Libor Masek and Peter Kovesi for their useful published MATLAB Source Codes. Portions of the research in this paper used the CASIA iris image database collected by Institute of Automation, Chinese Academy of Sciences.

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Authors and Affiliations

  1. Electrical Engineering Department, University of Jordan, Amman, Jordan

    R. T. Al-Zubi & D. I. Abu-Al-Nadi

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  1. R. T. Al-Zubi
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  2. D. I. Abu-Al-Nadi
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Correspondence to D. I. Abu-Al-Nadi.

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Al-Zubi, R.T., Abu-Al-Nadi, D.I. Automated personal identification system based on human iris analysis. Pattern Anal Applic 10, 147–164 (2007). https://doi.org/10.1007/s10044-006-0058-2

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  • DOI: https://doi.org/10.1007/s10044-006-0058-2

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